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Found 6 papers, 1 papers with code
Efficient entity-based reinforcement learning
Recent deep reinforcement learning (DRL) successes rely on end-to-end learning from fixed-size observational inputs (e. g. image, state-variables).
Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood.
State representation learning with recurrent capsule networks
Unsupervised learning of compact and relevant state representations has been proved very useful at solving complex reinforcement learning tasks.
Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction
Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood.
Learning Representations of Spatial Displacement through Sensorimotor Prediction
Robots act in their environment through sequences of continuous motor commands.
Representation Learning in Partially Observable Environments using Sensorimotor Prediction
We propose a model which integrates sensorimotor information over time, and project it in a sensory representation which is useful for prediction.
